

'''simple docstring'''





import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class  UpperCAmelCase (							UpperCAmelCase__  , unittest.TestCase    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					LEDTokenizer
		SCREAMING_SNAKE_CASE_           =					LEDTokenizerFast
		SCREAMING_SNAKE_CASE_           =					True
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							super().setUp()
							lowerCamelCase_  				=      [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							lowerCamelCase_  				=      dict(zip(SCREAMING_SNAKE_CASE_   ,			range(len(SCREAMING_SNAKE_CASE_		)		)		)		)
							lowerCamelCase_  				=      ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							lowerCamelCase_  				=      {'unk_token': '<unk>'}

							lowerCamelCase_  				=      os.path.join(self.tmpdirname   ,			VOCAB_FILES_NAMES['vocab_file']		)
							lowerCamelCase_  				=      os.path.join(self.tmpdirname   ,			VOCAB_FILES_NAMES['merges_file']		)
							with open(self.vocab_file   ,			'w'   ,			encoding='utf-8'		) as fp:
												fp.write(json.dumps(SCREAMING_SNAKE_CASE_		) + '\n'		)
							with open(self.merges_file   ,			'w'   ,			encoding='utf-8'		) as fp:
												fp.write('\n'.join(SCREAMING_SNAKE_CASE_		)		)
		def 			UpperCamelCase(					self   ,			**SCREAMING_SNAKE_CASE_		)  ->     int:






							'''simple docstring'''



							kwargs.update(self.special_tokens_map		)
							return self.tokenizer_class.from_pretrained(self.tmpdirname   ,			**SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self   ,			**SCREAMING_SNAKE_CASE_		)  ->     Tuple:






							'''simple docstring'''



							kwargs.update(self.special_tokens_map		)
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,			**SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     Optional[int]:






							'''simple docstring'''



							return "lower newer", "lower newer"
		@cached_property
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							return LEDTokenizer.from_pretrained('allenai/led-base-16384'		)
		@cached_property
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'		)
		@require_torch
		def 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							lowerCamelCase_  				=      ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							lowerCamelCase_  				=      [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												lowerCamelCase_  				=      tokenizer(SCREAMING_SNAKE_CASE_   ,			max_length=len(SCREAMING_SNAKE_CASE_		)   ,			padding=SCREAMING_SNAKE_CASE_   ,			return_tensors='pt'		)
												self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)

												self.assertEqual((2, 9)   ,			batch.input_ids.shape		)
												self.assertEqual((2, 9)   ,			batch.attention_mask.shape		)
												lowerCamelCase_  				=      batch.input_ids.tolist()[0]
												self.assertListEqual(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
		@require_torch
		def 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      ['A long paragraph for summarization.', 'Another paragraph for summarization.']
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												lowerCamelCase_  				=      tokenizer(SCREAMING_SNAKE_CASE_   ,			padding=SCREAMING_SNAKE_CASE_   ,			return_tensors='pt'		)
												self.assertIn('input_ids'   ,			SCREAMING_SNAKE_CASE_		)
												self.assertIn('attention_mask'   ,			SCREAMING_SNAKE_CASE_		)
												self.assertNotIn('labels'   ,			SCREAMING_SNAKE_CASE_		)
												self.assertNotIn('decoder_attention_mask'   ,			SCREAMING_SNAKE_CASE_		)
		@require_torch
		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      [
							    'Summary of the text.',
							    'Another summary.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												lowerCamelCase_  				=      tokenizer(text_target=SCREAMING_SNAKE_CASE_   ,			max_length=32   ,			padding='max_length'   ,			return_tensors='pt'		)
												self.assertEqual(32   ,			targets['input_ids'].shape[1]		)
		@require_torch
		def 			UpperCamelCase(					self		)  ->     List[Any]:






							'''simple docstring'''



							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												lowerCamelCase_  				=      tokenizer(
												    ['I am a small frog' * 1024, 'I am a small frog']   ,			padding=SCREAMING_SNAKE_CASE_   ,			truncation=SCREAMING_SNAKE_CASE_   ,			return_tensors='pt'		)
												self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
												self.assertEqual(batch.input_ids.shape   ,			(2, 5122)		)
		@require_torch
		def 			UpperCamelCase(					self		)  ->     Union[str, Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      ['A long paragraph for summarization.']
							lowerCamelCase_  				=      [
							    'Summary of the text.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												lowerCamelCase_  				=      tokenizer(SCREAMING_SNAKE_CASE_   ,			return_tensors='pt'		)
												lowerCamelCase_  				=      tokenizer(text_target=SCREAMING_SNAKE_CASE_   ,			return_tensors='pt'		)
												lowerCamelCase_  				=      inputs['input_ids']
												lowerCamelCase_  				=      targets['input_ids']
												self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
												self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
												self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
												self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)
		@require_torch
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
												lowerCamelCase_  				=      ['Summary of the text.', 'Another summary.']
												lowerCamelCase_  				=      [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

												lowerCamelCase_  				=      tokenizer(SCREAMING_SNAKE_CASE_   ,			padding=SCREAMING_SNAKE_CASE_		)
												lowerCamelCase_  				=      [[0] * len(SCREAMING_SNAKE_CASE_		) for x in encoded_output['input_ids']]
												lowerCamelCase_  				=      tokenizer.pad(SCREAMING_SNAKE_CASE_		)
												self.assertSequenceEqual(outputs['global_attention_mask']   ,			SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



							pass






		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
																	lowerCamelCase_  				=      self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)
																	lowerCamelCase_  				=      self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)
																	lowerCamelCase_  				=      'A, <mask> AllenNLP sentence.'
																	lowerCamelCase_  				=      tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_   ,			add_special_tokens=SCREAMING_SNAKE_CASE_   ,			return_token_type_ids=SCREAMING_SNAKE_CASE_		)
																	lowerCamelCase_  				=      tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE_   ,			add_special_tokens=SCREAMING_SNAKE_CASE_   ,			return_token_type_ids=SCREAMING_SNAKE_CASE_		)
																	self.assertEqual(sum(tokens_r['token_type_ids']		)   ,			sum(tokens_p['token_type_ids']		)		)
																	self.assertEqual(
																	    sum(tokens_r['attention_mask']		) / len(tokens_r['attention_mask']		)   ,			sum(tokens_p['attention_mask']		) / len(tokens_p['attention_mask']		)   ,			)

																	lowerCamelCase_  				=      tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']		)
																	lowerCamelCase_  				=      tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']		)
																	self.assertSequenceEqual(tokens_p['input_ids']   ,			[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)
																	self.assertSequenceEqual(tokens_r['input_ids']   ,			[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)

																	self.assertSequenceEqual(
																	    SCREAMING_SNAKE_CASE_   ,			['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']		)
																	self.assertSequenceEqual(
																	    SCREAMING_SNAKE_CASE_   ,			['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']		)








'''simple docstring'''



def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							return x if y == 0 else greatest_common_divisor(A       ,		x % y					)
def        UpperCAmelCase (    A	:					int       ,		A	:					int					):
							return (x * y) // greatest_common_divisor(A       ,		A					)
def        UpperCAmelCase (    A	:					int = 20					):
							SCREAMING_SNAKE_CASE       :   Tuple         =      1
							for i in range(1       ,		n + 1					):
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      lcm(A       ,		A					)
							return g


if __name__ == "__main__":
							print(f'{solution() = }')




